DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction

Abstract

The invention relates to the technical field of power distribution networks, in particular to a DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction. The method comprises the following steps: judging whether EV can discharge; collaborative optimization of large-scale EV charging and discharging and SOP topology reconstruction is carried out on the EV capable of discharging, and then an optimal charging plan and an optimal discharging plan are obtained; on the basis of the optimal charging plan and discharging plan, establishing a double-layer optimization model taking distribution network system loss, total voltage deviation and user income as multiple targets; a modified IEEE 33-node direct-current system simulation model is established, and it is verified that the proposed direct-current distribution network collaborative optimization method can effectively reduce system performance indexes such as system network loss and total voltage deviation and improve the earnings of EV users. According to the method, the vehicle network interaction characteristic of the EV and the regulation and control characteristic of the SOP on the power flow distribution are considered, and a collaborative optimization method of EV charging and discharging and SOP access is considered, so that system performance indexes such as system network loss and total voltage deviation of the direct current distribution network are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, and in particular to a DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction. Background Art

[0002] In the optimization research of DC loads such as large-scale EVs accessing the DC distribution network, EVs accessing the grid in the form of loads for charging will inevitably lead to a drop in the voltage of the terminal node and increase network losses. At the same time, EVs can also be used as distributed power supply equipment to transmit electric energy to the grid, helping to alleviate the pressure of the power load on the grid and the impact of disordered charging, thereby achieving the purpose of peak shaving and valley filling. As a fully controlled power electronic device, SOP can realize functions such as power conversion, flow control and fault isolation. Considering the high cost of SOP construction and the complex control process, it is necessary to determine the optimal location and number of SOP access before actual engineering construction to ensure that the DC distribution network can operate reliably and economically. In solving the optimization of DC loads such as large-scale EVs accessing the DC distribution network, EV discharge and SOP changing the topology structure can achieve the optimization of DC distribution network performance indicators. Therefore, the synergy of the two in system optimization can be considered at the same time.

[0003] In order to better reduce the impact of large-scale EV and other DC loads connected to the DC distribution network, it is necessary to simultaneously consider the vehicle-grid interaction characteristics of EV and the regulation characteristics of SOP on power flow distribution, and explore a collaborative optimization method considering EV charging and discharging and SOP access to better reduce system performance indicators such as system network loss and total voltage deviation of the DC distribution network; on the other hand, from the perspective of EV users or operators, this method can also ensure that EV users obtain higher benefits by participating in charging and discharging. Therefore, we propose a DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction. Summary of the invention

[0004] The purpose of the present invention is to provide a DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction to solve the problems raised in the above background technology.

[0005] To achieve the above purpose, a DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction is provided, comprising the following method steps:

[0006] S1, determine whether the EV can be discharged;

[0007] S2: Coordinated optimization of large-scale EV charging and discharging and SOP topology reconstruction for EVs that can discharge, and then obtain the best charging plan and discharging plan;

[0008] S3. Based on the optimal charging plan and discharging plan, a two-layer optimization model with distribution network system loss, total voltage deviation and user benefits as multiple objectives is established;

[0009] S4. Establish a modified IEEE 33-bus DC system simulation model to verify that the proposed DC distribution network collaborative optimization method can effectively reduce system performance indicators such as system network loss and total voltage deviation, and improve the benefits of EV users.

[0010] As a further improvement of the technical solution, the S1 determines whether the EV can be discharged, and the method steps are as follows:

[0011] S1.1.1. Consider whether the remaining battery power of the EV meets the discharge requirements when discharging;

[0012] S1.1.2. For EVs that meet the discharge requirements, the owner subjectively controls whether the EV participates in the discharge.

[0013] As a further improvement of the technical solution, S2 performs coordinated optimization of large-scale EV charging and discharging and SOP topology reconstruction, and the method steps are as follows:

[0014] S2.1.1, determine the constraints for SOP topology reconstruction;

[0015] S2.1.2, determine the DC SOP model after transformation;

[0016] S2.1.3. Establish SOP equivalent model.

[0017] As a further improvement of the technical solution, the constraint conditions for determining the SOP topology reconstruction in S2.1.1 include: active power constraint conditions at both ends of the SOP and capacity constraint conditions at both ends of the SOP.

[0018] As a further improvement of the technical solution, S2.1.2 determines the modified DC SOP model. Based on the dual-port SOP model used in the traditional AC system, both AC voltage source converters are transformed into DC-DC converters that meet the DC power distribution requirements, thereby obtaining the modified DC SOP model.

[0019] As a further improvement of the present technical solution, S2.1.3 establishes a SOP equivalent model, regards the SOP as a voltage-type power flow controller, and by analyzing the impact of the power flow controller on the DC distribution network power flow, its external characteristics are equated with a DC transformer with an adjustable transformation ratio, thereby obtaining a SOP equivalent model.

[0020] As a further improvement of the present technical solution, S3 establishes a two-layer optimization model with distribution network system loss, total voltage deviation and user benefit as multiple objectives. The upper model takes distribution network system loss, total voltage deviation and user benefit as multi-objective optimization functions, and the lower model takes minimizing distribution network system loss as the goal.

[0021] As a further improvement of the present technical solution, the upper model includes establishing a distribution network system loss Pt,loss minimization model, establishing a mathematical model of the total voltage deviation fQ, and establishing a mathematical model of the EV user benefit fE.

[0022] As a further improvement of the technical solution, the lower layer model aims to minimize the loss of the distribution network system and uses simulated annealing method to optimize the access location and number of SOPs in the DC distribution network.

[0023] As a further improvement of the present technical solution, the S4 establishes a modified IEEE 33-node DC distribution system to analyze and verify the feasibility of the proposed collaborative optimization method for large-scale EV charging and discharging and SOP topology reconstruction.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction first judges the remaining battery power of the EV. For EVs that meet the discharge requirements, the owner subjectively controls whether the EV participates in the discharge, thereby protecting the EV battery and preventing it from being damaged by excessive discharge. The mobile energy storage characteristics of EV and the power flow distribution characteristics of SOP are used to directly affect the power flow distribution of the power system by adjusting the power exchange of the feeders on both sides. The node voltage is maintained or changed by adjusting the power exchange, thereby optimizing the voltage distribution of the system, and can better reduce the system network loss, total voltage deviation and other system performance indicators of the DC distribution network. The algorithm is used to solve the optimal EV discharge power connected to each discharge station in the upper model and the optimal position and number of SOP access in the lower model, thereby improving the benefits of EV users. The effectiveness of the proposed DC distribution network collaborative optimization method is specifically verified by a modified IEEE 33-node DC system example. The invention results have guiding significance for the research on the optimization technology of DC loads such as large-scale EVs connected to the DC distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction proposed in the present invention;

[0027] Figure 2 A flow chart for determining whether an EV can be discharged in the present invention;

[0028] Figure 3It is a flow chart of the collaborative optimization method of large-scale EV charging and discharging and SOP topology reconstruction in the present invention;

[0029] Figure 4 It is a basic structure diagram of SOP applicable to DC system established in the present invention;

[0030] Figure 5 is a comparison diagram of system losses in different scenarios in the present invention;

[0031] Figure 6 It is a comparison diagram of total voltage deviation in different scenarios in the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] The present invention is achieved by adopting the following technical solutions: Figure 1 As shown, a DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction includes the following steps:

[0034] S1, determine whether the EV can be discharged;

[0035] S2: Coordinated optimization of large-scale EV charging and discharging and SOP topology reconstruction for EVs that can discharge, and then obtain the best charging plan and discharging plan;

[0036] S3. Based on the optimal charging plan and discharging plan, a two-layer optimization model with distribution network system loss, total voltage deviation and user benefits as multiple objectives is established;

[0037] S4. Establish a modified IEEE 33-bus DC system simulation model to verify that the proposed DC distribution network collaborative optimization method can effectively reduce system performance indicators such as system network loss and total voltage deviation, and improve the benefits of EV users.

[0038] In the context of large-scale EV and other DC loads connected to the DC distribution network, the embodiment of the present invention first determines the remaining battery power of the EV. For EVs that meet the discharge requirements, the owner then subjectively controls whether the EV participates in the discharge, thereby protecting the EV battery and preventing it from being damaged by excessive discharge. The mobile energy storage characteristics of the EV and the SOP control flow distribution characteristics are used to directly affect the power flow distribution of the power system by adjusting the power exchange of the feeders on both sides. The node voltage is maintained or changed by adjusting the power exchange, thereby optimizing the voltage distribution of the system, and better reducing the system network loss, total voltage deviation and other system performance indicators of the DC distribution network. The algorithm is used to solve the optimal EV discharge power connected to each discharge station in the upper model and the optimal position and number of SOP access in the lower model, thereby improving the benefits of EV users. The effectiveness of the proposed DC distribution network collaborative optimization method is specifically verified by a modified IEEE 33-node DC system example. The invention results have guiding significance for the research on the optimization technology of large-scale EV and other DC loads connected to the DC distribution network.

[0039] For the first embodiment, please refer to Figure 2 As shown, S1 determines whether the EV can be discharged, and the method steps are as follows:

[0040] S1.1.1. Consider whether the remaining battery power of the EV meets the discharge requirements when discharging;

[0041] S1.1.2. For EVs that meet the discharge requirements, the owner subjectively controls whether the EV participates in the discharge.

[0042] Among them, considering that EV participates in the power grid in the form of charging load and energy storage equipment, the charging and discharging power of EV connected to the power grid remains constant over a period of time, that is, the total discharge power Wd(t) at the discharge station d at time t is expressed as:

[0043]

[0044] Where D represents the set of all discharge stations; N is the number of all EVs participating in discharge at discharge station d; kj,t is the discharge state of the j-th EV in period t, which is 1 when discharging and 0 otherwise; Pd j,t is the discharge power of the j-th EV at discharge station d in period t, in kW.

[0045] By detecting the discharge status of EVs in all discharge stations D, the discharge power Pd j,t of the EVs in discharge is superimposed and calculated to obtain the total discharge power Wd(t) at the discharge station d at time t. By counting the total discharge power Wd(t) at each discharge station d, an EV discharge power model is established.

[0046] Whether an EV can be discharged not only considers whether the owner is willing to participate in the discharge, but also whether the remaining battery power of the EV meets the discharge requirements. Considering the impact of EV discharge on battery life, the discharge capacity of EV is expressed as the remaining dischargeable power Qd j,t:

[0047]

[0048] Where Qd j,t is the remaining dischargeable power of the j-th EV at the discharge station d during period t, in kW·h; SOCdj,t is the battery state of charge of the j-th EV at the discharge station d before discharge during period t; QEV j is the rated capacity of the battery of the j-th EV, in kW·h.

[0049] When the battery state of charge of the EV before discharge at the discharge station d is less than or equal to 0.2, the remaining dischargeable power of the EV at the discharge station d is considered to be zero, that is, the remaining battery power of the EV does not meet the discharge requirements. When the battery state of charge of the EV before discharge at the discharge station d is greater than 0.2, the battery charge before discharge at d is subtracted by 0.2, and then multiplied by the rated capacity of the EV battery to obtain the remaining dischargeable power of the EV at the discharge station d at this time, and then the EV discharge response model is established.

[0050] For the second embodiment, please refer to Figure 3 As shown, S2 performs coordinated optimization of large-scale EV charging and discharging and SOP topology reconstruction, and the method steps are as follows:

[0051] S2.1.1, determine the constraints for SOP topology reconstruction;

[0052] S2.1.2, determine the DC SOP model after transformation;

[0053] S2.1.3. Establish SOP equivalent model

[0054] S2.1.1. Determine the constraints for SOP topology reconstruction, including: active power constraints at both ends of the SOP and capacity constraints at both ends of the SOP;

[0055] As a flexible multi-state switch, SOP can flexibly control power distribution and adjust node voltage. The active power constraints at both ends of SOP are:

[0056]

[0057]

[0058] Wherein, PSOP i,t and PSOP j,t are the active powers injected by the SOP at nodes i and j in period t, respectively, in kW; PSOP,L i,t and PSOP,L j,t are the losses of the SOP at nodes i and j in period t, respectively, in kW; ASOPi and ASOP j are the loss coefficients of the SOP at nodes i and j, respectively.

[0059] The loss of the SOP at node i and node j is equal to the value obtained by multiplying the loss coefficient of the SOP at node i and node j by the active power injected by the SOP at node i and node j, and the sum of the active power injected by the SOP at node i and node j and the loss of the SOP at node i and node j is zero, thereby maintaining or changing the node voltage by controlling the power flow distribution and adjusting the power exchange.

[0060] Among them, the capacity constraints at both ends of the SOP are:

[0061]

[0062] Where SSOP ij is the SOP capacity installed between nodes i and j, in kW.

[0063] The active power injected by the SOP at nodes i and j must be numerically less than or equal to the capacity of the SOP installed between nodes i and j, thereby ensuring that the SOP can maintain or change the node voltage by controlling the power flow distribution and regulating the power exchange.

[0064] S2.1.2 Determine the modified DC SOP model. Based on the two-port SOP model used in the traditional AC system, transform the two AC voltage source converters into DC-DC converters that meet the DC power distribution requirements, thereby obtaining the modified DC SOP model, such as Figure 4 shown.

[0065] S2.1.3 Establish an SOP equivalent model. In order to facilitate the flow calculation, SOP can be equivalent to a voltage-changing flow controller. By analyzing the impact of the flow controller on the DC distribution network flow, its external characteristics are equivalent to a DC transformer with an adjustable ratio, thereby obtaining the SOP equivalent model.

[0066] Among them, S3 establishes a two-layer optimization model with distribution network system loss, total voltage deviation and user benefits as multi-objectives. The upper model takes distribution network system loss, total voltage deviation and user benefits as multi-objective optimization functions, and the lower model takes the minimum distribution network system loss as the goal. The multi-objective particle swarm optimization algorithm is used to solve the optimal EV discharge power connected to each discharge station. Among them, the distribution network system loss Pt,loss minimization model is established:

[0067]

[0068]

[0069]

[0070] Wherein, PL t,loss is the network loss of the distribution network branch, in kW; PSOP t,loss is the SOP loss, in kW; Ωline represents the branch set without SOP; Gij is the mutual conductance between nodes i and j in the node conductance matrix, in S; Ui,t and Uj,t are the voltage amplitudes of nodes i and j at time t, in V; ΩSOP represents the branch set with SOP.

[0071] The distribution network branch loss is obtained by multiplying the square of the difference in voltage amplitude between nodes i and j of the branch set without SOP by the mutual conductance between nodes i and j and then summing them up. The SOP loss is then obtained by adding the sum of the SOP losses of nodes i and j of the branch set with SOP. The minimum system loss is obtained by adding the distribution network branch loss to the SOP loss, thereby establishing a distribution network system loss Pt,loss minimization model.

[0072] Among them, the mathematical model of the total voltage deviation fQ is established:

[0073]

[0074] Wherein, Ωnode represents the set of all nodes; Ui,n is the rated voltage amplitude of node i, in V.

[0075] The voltage difference between all nodes i and j is compared with the rated voltage amplitude of node i, and then summed to obtain the total voltage deviation, thereby establishing a mathematical model of the total voltage deviation fQ.

[0076] Among them, the mathematical model of EV user benefit fE is established:

[0077] f E =ρ f,t N f,t P f,t Δt-ρ c,t N c,t P c,t Δt

[0078] Wherein, ρf,t and ρc,t are the discharge and charging prices of EV in period t, respectively, in yuan; Nf,t and Nc,t are the total number of EVs discharged and charged in period t, respectively; Pf,t and Pc,t are the discharge and charging powers of EVs in period t, respectively, in kW; Δt is the duration of period t, in h.

[0079] The total amount of discharges in a certain period of time is multiplied by the total discharge power and the discharge price to get the amount of money obtained by discharge. The total amount of charging in a certain period of time is multiplied by the total charging power and the charging price to get the amount of money spent on charging. The amount of money obtained by discharge minus the amount of money spent on charging is used to get the user benefit, thereby establishing a mathematical model of EV user benefit fE.

[0080] The multi-objective particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence, which performs well in solving multi-objective optimization problems. The core idea of ​​the algorithm is to find the optimal solution to the problem through collaboration and information sharing between particle swarms. In MOPSO, each particle represents a potential solution, and they move in the search space and follow the currently searched optimal particle, in this way to find the global optimal solution.

[0081] The lower model aims to minimize the loss of the distribution network system and uses the simulated annealing method to optimize the access location and number of SOPs in the DC distribution network.

[0082] The simulated annealing algorithm starts from a relatively high initial temperature, and as the temperature parameter continues to decrease, it combines the probability jump characteristics to randomly search for the global optimal solution of the objective function in the solution space, that is, it can probabilistically jump out of the local optimal solution and eventually tend to the global optimal solution.

[0083] Among them, S4 established a modified IEEE 33-node DC distribution system to analyze and verify the feasibility of the proposed collaborative optimization method for large-scale EV charging and discharging and SOP topology reconstruction. The two-layer optimization model was solved by multi-objective particle swarm algorithm and simulated annealing algorithm to obtain the Pareto non-inferior solution set and multi-objective function value. The system network loss and total voltage deviation under different scenarios were compared to analyze and verify the superiority of the proposed collaborative optimization method.

[0084] According to the above implementation method, the relevant parameters are set, and the feasibility of the proposed optimization method is further verified by writing an IEEE 33-node DC system simulation model program on MATLAB. The specific parameter settings are as follows:

[0085] The voltage level of the DC system is ±375V, the voltage of node 1 is 1.00pu, and the allowable range of node voltage deviation is set to [0.9, 1.05]pu; this system plans to connect EVs at nodes 9, 16, 20, 24, 28, and 33 for discharge, and the EV charging and discharging mode is fast charging and fast discharging; the SOP capacity is 1MW, and the loss coefficient A is 0.01; the time scale Δt is set to 0.5h. This embodiment is based on the relevant parameter values ​​described above and is carried out step by step according to the above implementation steps. The multi-objective two-layer optimization model is solved to obtain 192 sets of Pareto non-inferior solutions. The non-inferior solution sets and objective functions of 12 groups of optimization schemes randomly selected during the peak load period are shown in Tables 1 and 2 respectively; the system loss and total voltage deviation results in different scenarios are shown in Tables 1 and 2. Figure 5 , 6 As shown;

[0086] Table 1

[0087]

[0088] Table 2

[0089]

[0090] It can be seen from Tables 1 and 2 that when DC loads such as large-scale EVs are connected to the DC distribution network, the EV discharge characteristics can be used to input the best power to the system, and the SOP topology reconstruction characteristics can be used to perform the best combination of switches to minimize system losses and total voltage deviations and maximize user benefits, thereby achieving the coordinated optimization of EV charging and discharging and SOP topology reconstruction, and verifying the feasibility of the proposed collaborative optimization method; analysis Figure 5 , 6 It can be seen that when large-scale EVs are connected to the DC distribution network for charging and discharging, the scenario of connecting to the SOP can greatly reduce system losses and total voltage deviations, realize the coordinated optimization of the DC distribution system, and verify the superiority of the proposed collaborative optimization method. The present invention can be applied to the optimization technology research of large-scale EV and other DC loads connected to the DC distribution network, and provides a new method for optimizing the connection of large-scale EV and other DC loads to the DC distribution network.

[0091] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction, characterized in that: The following steps are involved: S1, determine whether the EV can be discharged; S2: Coordinated optimization of large-scale EV charging and discharging and SOP topology reconstruction for EVs that can discharge, and then obtain the best charging plan and discharging plan; S3. Based on the optimal charging plan and discharging plan, a two-layer optimization model with distribution network system loss, total voltage deviation and user benefits as multiple objectives is established; S4. Establish a modified IEEE 33-bus DC system simulation model to verify that the proposed DC distribution network collaborative optimization method can effectively reduce system performance indicators such as system network loss and total voltage deviation, and improve the benefits of EV users.

2. The DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction according to claim 1 is characterized in that: The S1 determines whether the EV can be discharged, and the method steps are as follows: S1.1.

1. Consider whether the remaining battery power of the EV meets the discharge requirements when discharging; S1.1.

2. For EVs that meet the discharge requirements, the owner subjectively controls whether the EV participates in the discharge.

3. The DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction according to claim 1 is characterized in that: The S2 performs coordinated optimization of large-scale EV charging and discharging and SOP topology reconstruction, and the method steps are as follows: S2.1.1, determine the constraints for SOP topology reconstruction; S2.1.2, determine the DC SOP model after transformation; S2.1.

3. Establish SOP equivalent model.

4. The DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction according to claim 3 is characterized in that: The constraint conditions for determining the topology reconstruction of the SOP in S2.1.1 include: active power constraint conditions at both ends of the SOP and capacity constraint conditions at both ends of the SOP.

5. The DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction according to claim 3 is characterized in that: The S2.1.2 determines the transformed DC SOP model. Based on the two-port SOP model used in the traditional AC system, both AC voltage source converters are transformed into DC-DC converters that meet the DC power distribution requirements, thereby obtaining the transformed DC SOP model.

6. The DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction according to claim 3 is characterized in that: The S2.1.3 establishes a SOP equivalent model, regards SOP as a voltage-changing power flow controller, analyzes the impact of the power flow controller on the DC distribution network power flow, and equates its external characteristics with a DC transformer with adjustable transformation ratio, thereby obtaining a SOP equivalent model.

7. The DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction according to claim 1 is characterized in that: The S3 establishes a two-layer optimization model with distribution network system loss, total voltage deviation and user benefit as multiple objectives. The upper model uses distribution network system loss, total voltage deviation and user benefit as multi-objective optimization functions, and the lower model uses the minimum distribution network system loss as the goal.

8. The DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction according to claim 7 is characterized in that: The upper model includes establishing a distribution network system loss Pt,loss minimization model, establishing a mathematical model of the total voltage deviation fQ, and establishing a mathematical model of the EV user benefit fE.

9. The DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction according to claim 7 is characterized in that: The lower model aims to minimize the loss of the distribution network system and uses the simulated annealing method to optimize the access location and number of SOPs in the DC distribution network.

10. The DC distribution network collaborative optimization method considering EV charging and discharging and SOP topology reconstruction according to claim 1 is characterized in that: The S4 establishes a modified IEEE 33-node DC distribution system to analyze and verify the feasibility of the proposed collaborative optimization method for large-scale EV charging and discharging and SOP topology reconstruction.

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